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The aims of this chapter are to outline the key experimental methods used by neuroscientists to evaluate the impact of game-based training strategies on attention and working memory function in humans. The chapter is organized into sections that detail methods that probe the major facets of attention followed by methods that probe working memory. Attention methods are subdivided into sections on (1) spatial attention, (2) temporal attention, (3) combined spatial and temporal attention, (4) feature-/object-based attention, (5) sustained attention, (6) interaction between top-down and bottom-up attention, and (7) attention resource allocation. Methods that assess working memory function are then presented in two major sections, (9) visual and visuospatial working memory and (10) verbal working memory. We conclude with a discussion of prospects for further research and applications.

Almost entirely overlooked throughout the 20th century, neglected by contemporary medical manuals, the clitoris has gradually returned centre stage thanks to Western feminism.

Preliminary biostratigraphic and geochronological results obtained from a 44 m-long core drilled on the northern coast of Mayaguana Island (SE Bahamas) show that the topmost layers of the core date from the Burdigalian (Early Miocene), whereas the deepest units are of Chattian (Late Oligocene) or Aquitanian (Earliest Miocene) age. Accordingly, the platform aggraded 44 m of sediments in a 10 to 3 my time span, from the Chattian/Aquitanian to the Burdigalian, whereas previous surface investigations of the island showed that only 11 m of carbonates were accumulated in a 17 my-long period, between the Burdigalian and the Early Pleistocene. This new record shows that the accumulation rate of the Mayaguana Bank was much higher during the Late Paleogene/Early Miocene than during the time interval from the Middle Miocene to the Pleistocene. This decrease is likely due to vertical tectonic motions related to the late phases of the Cuban orogeny which reduced accommodation on the platform top. These results designate the Mayaguana Bank as an accurate gauge to record the elevation of sea-level highstands during the Neogene

The most salient geological features observed during a recent reconnaissance trip to Crooked Island, SE Bahamas, include: (1) altered bioclastic calcarenites of probable Early Pleistocene age; and (2) an elevated intertidal notch carved in last interglacial deposits, indicating that sea level peaked at a higher elevation than previously estimated during that time period. Four main lithostratigraphic units were identified on Crooked Island: (1) highly weathered bioclastic calcarenites that yielded unreliable alloisoleucine/isoleucine (A/I) ratios, and two valid 87Sr/86Sr ratios averaging 0.709147; (2) well-lithified bioclastic/peloidal eolianites, forming low sea cliffs, that gave one A/I ratio of 0.523; (3) a complex and extensive unit including scarce coral framestone, exposed up to +1.2 m above sea level, and oolitic-peloidal calcarenites deposited in subtidal, beach, and eolian environments that yielded A/I ratios averaging 0.411 (n=5); and (4) poorly lithified bioclastic beach ridges congruent with modern sea level. Moreover, a prominent ridge along the north coast of the island shows, at +11 m above sea level, an intertidal notch carved in Unit 3 eolianite and filled by Unit 3 beach facies. Units 4, 3 and 2 can be compared, respectively, to the Rice Bay (Holocene), the Grotto Beach (Late Pleistocene) and the Owl's Hole (Middle Pleistocene) formations, previously identified on many other Bahamian islands. Of probable Early Pleistocene age (between 0.6 and 1 Ma), Unit 1 could represent the lowermost part of the Owl's Hole Formation and the top of the underlying, mostly marine Misery Point Formation recently discovered on Mayaguana. The unequivocal occurrence of an intertidal notch carved in, and sealed by, last-interglacial deposits at +11 m shows that the peak elevation reached by sea level during that time interval was much higher than previously assessed. Finally, stratigraphic units decrease in age from N to S, suggesting that the island grew differently than other Bahamian islands or, alternatively, that the northern margin of the Crooked-Acklins bank collapsed in a recent past.

When developing NLP tools for low-resource languages, one is often confronted with the lack of annotated data. We propose to circumvent this bottleneck by training a supervised HMM tagger on a closely related language for which annotated data are available, and translating the words in the tagger parameter files into the low-resource language. The translation dictionaries are created with unsupervised lexicon induction techniques that rely only on raw textual data. We obtain a tagging accuracy of up to 89.08% using a Spanish tagger adapted to Catalan, which is 30.66% above the performance of an unadapted Spanish tagger, and 8.88% below the performance of a supervised tagger trained on annotated Catalan data. Furthermore, we evaluate our model on several Romance, Germanic and Slavic languages and obtain tagging accuracies of up to 92%.

We consider methods for handling incomplete (elliptical) utterances in spoken phraselators, and describe how they have been implemented inside BabelDr, a substantial spoken medical phraselator. The challenge is to extend the phrase matching process so that it is sensitive to preceding dialogue context. We contrast two methods, one using limited-vocabulary strict grammar-based speech and language processing and one using large-vocabulary speech recognition with fuzzy grammar-based processing, and present an initial evaluation on a spoken corpus of 821 context-sentence/elliptical-phrase pairs. The large-vocabulary/fuzzy method strongly outperforms the limited-vocabulary/strict method over the whole corpus, though it is slightly inferior for the subset that is within grammar coverage. We investigate possibilities for combining the two processing paths, using several machine learning frameworks, and demonstrate that hybrid methods strongly outperform the large-vocabulary/fuzzy method.

This paper reports on challenges and results in developing NLP resources for spoken Rusyn. Being a Slavic minority language, Rusyn does not have any resources to make use of. We propose to build a morphosyntactic dictionary for Rusyn, combining existing resources from the etymologically close Slavic languages Russian, Ukrainian, Slovak, and Polish. We adapt these resources to Rusyn by using vowel-sensitive Levenshtein distance, hand-written language-specific transformation rules, and combinations of the two. Compared to an exact match baseline, we increase the coverage of the resulting morphological dictionary by up to 77.4% relative (42.9% absolute), which results in a tagging recall increased by 11.6% relative (9.1% absolute). Our research confirms and expands the results of previous studies showing the efficiency of using NLP resources from neighboring languages for low-resourced languages. Peer reviewed

International audience; This essay examines the geographic origins, the political belongings and the confessional profiles of the members of the two most important mercantile nations of eighteenth-century southern Italy: the French nation and the British factory of Naples. Taking into account their pronounced prosopographic heterogeneity, it shows how legal resources and their social uses played a crucial role in defining the boundaries of such groups.

Recurrent neural networks (RNNs) have achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate here to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues ("The colorless green ideas I ate with the chair sleep furiously"), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence. Comment: Accepted to NAACL 2018